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Chinese Journal of Electronics 2024年第1期33卷 128-138页

作者： Jinxue PENG Yong WANG Jingfeng XUE Zhenyan LIU school of Computer Science and technology Beijing Institute of Technology

Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones. Due to a large amount of convolutional computation and the loss of structural information, the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy. To address these issues, we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN) for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features. GNN’s node-centric and small batch is a suitable training way for large CFGs, it can greatly reduce computational overhead. Various NLP basic block embedding models and GNNs are evaluated. Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE) for function representation outperforms the state-of-the-art works. In our framework, we can take only 45% overhead. Improve efficiency significantly with a small performance trade-off.

关键词： Training Semantics Process control Binary codes Feature extraction Natural language processing Graph neural networks

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High-Performance All-Printed Flexible Micro-Supercapacitors with Hierarchical Encapsulation

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Energy & Environmental Materials 2024年第4期7卷 262-268页

作者： Yuhang Yuan Wei Yuan Yaopeng Wu Xuyang Wu Xiaoqing Zhang Simin Jiang Bote Zhao Yu Chen Chenghao Yang Liangxin Ding Zhenghua Tang Yingxi Xie Yong Tang school of Mechanical & Automotive Engineering South China University of Technology school of Environment and Energy South China University of Technology school of Chemistry and Chemical Engineering South China University of Technology

Printed micro-supercapacitors (MSCs) have shown broad prospect in flexible and wearable *** of previous studies focused on printing the electrochemically active materials paying less attention to other key components like current collectors and *** study presents an allprinting strategy to fabricate in-plane flexible and substrate-free MSCs with hierarchical encapsulation.

关键词： all-printing flexible hierarchical encapsulation micro-supercapacitors substrate-free

Triphenylphosphine-Assisted Exsolution Engineering on Ruddlesden-Popper Perovskites for Promoting Oxygen Evolution

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Energy & Environmental Materials 2024年第4期7卷 433-441页

作者： Juan Bai Jing Shang Jun Mei Dongchen Qi Ting Liao Ziqi Sun school of Chemistry and Physics Queensland University of Technology Centre for Materials Science Queensland University of Technology school of Materials Science and Engineering Shaanxi University of Science and Technology school of Mechanical Medical and Process Engineering Queensland University of Technology

Metal exsolution engineering has been regarded as a promising strategy for activating intrinsically inert perovskite oxide catalysts toward efficient oxygen evolution *** metal exsolution processes on perovskites are often achieved by using the reducing hydrogen gas; however,this is not effective for the relatively stable phase,such as Ruddlesden-Popper perovskite oxides.

关键词： exsolution oxygen evolution perovskite reconstruction Ruddlesden–Popper

Satellite anomaly detection based on reconstruction discrepancy theory utilizing a new dual-branch reconstruction model

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Science China Technological Sciences 2024年第10期67卷 3294-3307页

作者： ZHAO HaoTian QIU Shi LIU Ming CAO XiBin Research Center of Satellite technology School of AstronauticsHarbin Institute of Technology

To enhance the accuracy of deep learning methods based on reconstruction discrepancy in satellite anomaly detection tasks, this study proposes a dual-branch reconstruction model(DBRM) and designs a comprehensive satellite anomaly detection framework around this model. Firstly, we introduce the temporal-channel mixer(TC-Mixer) module, which mainly comprises a self-attention layer for capturing long-range temporal dependencies in telemetry data, and two types of feed-forward networks(FFN) for extracting complex patterns in the temporal and channel dimension of telemetry data. This design endows the TC-Mixer module with robust capabilities for extracting complicated dependencies in telemetry data. Secondly, with the TC-Mixer module as the main component, we designed the DBRM. This model utilizes a shared latent representation layer, allowing the regeneration branch and forecasting branch of the DBRM to share most of the feature extraction network architecture. This approach significantly enhances the model's regression accuracy while reducing computational complexity. Thirdly, using the DBRM as the core network model, we devised a comprehensive satellite anomaly detection framework. This includes an anomaly criterion that considers the reconstruction discrepancy of both the regeneration and forecasting branches, the peak-over-threshold(POT) method for anomaly thresholding, and the MIC-based feature engineering method, etc. Finally, we conducted comparative experiments with several SOTA anomaly detection algorithms on two public and one private satellite anomaly detection datasets. The experimental results validate the effectiveness and superiority of our proposed method.

关键词： anomaly detection reconstruction discrepancy dual-branch reconstruction model (DBRM) satellite telemetry data time series

Multi-Robot Cooperative Localization: Time-Varying Unobservable Subspace Analysis and Consistent Algorithm Design

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Journal of Systems Science & Complexity 2024年第5期37卷 1937-1955页

作者： HAO Ning HE Fenghua YAO Yu school of Astronautics Harbin Institute of Technology

This paper investigates the problem of cooperative localization(CL) for a multi-robot system(MRS) under dynamic measurement topology, which involves a group of robots collectively estimating their poses with respect to a common reference frame using ego-motion measurements and robot-to-robot relative measurements. The authors provide a theoretical analysis of the time-varying unobservable subspace and propose a consistent cooperative localization algorithm. First, the authors introduce the relative measurement graph(RMG) to represent the relative pose measurements obtained by the MRS at each instant. Then, the authors derive the local observability matrix over a time *** equivalent relationship is established between the local observability matrix and the spectral matrices of the RMG. Moreover, the authors present a method for constructing the unobservable subspace based on the RMG under different topology conditions. Based on this analysis, the authors design a consistent cooperative localization algorithm that satisfies the constraints of the time-varying unobservable subspace. An analytical optimal solution is derived for the constrained optimization *** Carlo numerical simulations are conducted to demonstrate the consistency and accuracy of the proposed method.

关键词： Consistency cooperative localization multi-robot system null space relative measurement graph time-varying unobservable subspace

HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU school of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology school of Computer Science and technology Wuhan University of Science and Technology school of Future technology South China University of Technology Shenzhen Research Institute of Hunan University school of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

Effect of strain on structure and electronic properties of monolayer C_(4)N_(4)

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Chinese Physics B 2024年第5期33卷 595-600页

作者：陈昊徐瑛赵家石周丹 school of Physics Changchun University of Science and TechnologyChangchun 130022China school of Computer Science and technology Changchun University of Science and TechnologyChangchun 130022China

The first-principles calculations are performed to examine structural,mechanical,and electronic properties at large strain for a monolayer C_(4)N_(4),which has been predicted as an anchoring promising material to attenuate shuttle effect in Li–S batteries stemming from its large absorption energy and low diffusion energy *** results show that the ideal strengths of C_(4)N_(4)under tension and pure shear deformation conditions reach 13.9 GPa and 12.5 GPa when the strains are 0.07 and 0.28,*** folded five-membered rings and diverse bonding modes between carbon and nitrogen atoms enhance the ability to resist plastic deformation of C_(4)N_(4).The orderly bond-rearranging behaviors under the weak tensile loading path along the[100]direction cause the impressive semiconductor–metal transition and inverse semiconductor–metal *** present results enrich the knowledge of the structure and electronic properties of C_(4)N_(4)under deformations and shed light on exploring other two-dimensional materials under diverse loading conditions.

关键词： two-dimensional materials strain effect structural evolution electronic properties

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A lightweight symmetric image encryption cryptosystem in wavelet domain based on an improved sine map

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Chinese Physics B 2024年第3期33卷 266-276页

作者：陈柏池黄林青蔡述庭熊晓明张慧 school of Advanced Manufacturing Guangdong University of TechnologyJieyang 522000China school of Integrated Circuits Guangdong University of TechnologyGuangzhou 510006China school of Automation Guangdong University of TechnologyGuangzhou 510006China

In the era of big data,the number of images transmitted over the public channel increases *** a result,it is crucial to devise the efficient and highly secure encryption method to safeguard the sensitive *** this paper,an improved sine map(ISM)possessing a larger chaotic region,more complex chaotic behavior and greater unpredictability is proposed and extensively *** upon the strengths of ISM,we introduce a lightweight symmetric image encryption cryptosystem in wavelet domain(WDLIC).The WDLIC employs selective encryption to strike a satisfactory balance between security and ***,only the low-frequency-low-frequency component is chosen to encrypt utilizing classic permutation and *** leveraging the statistical properties in wavelet domain,Gaussianization operation which opens the minds of encrypting image information in wavelet domain is first proposed and employed to all *** and theoretical analysis demonstrate the high speed and the remarkable effectiveness of WDLIC.

关键词： image encryption discrete wavelet transform 1D-chaotic system selective encryption Gaussianization operation

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A novel method for extracting and optimizing the complex permittivity of paper-based composites based on an artificial neural network model

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Science China Technological Sciences 2024年第10期67卷 3190-3204页

作者： XIA ChenBin SHEN JunYi LIAO ShaoWei WANG Yi HUANG ZhengSheng XUE Quan TANG Min LONG Jin HU Jian school of Light Industry and Engineering South China University of Technology school of Electronic and Information Engineering South China University of Technology

Measuring the complex permittivity of ultrathin, flexible materials with a high loss tangent poses a substantial challenge with precision using conventional methods, and verifying the accuracy of test results remains difficult. In this study, we introduce a methodology based on a back-propagation artificial neural network(ANN) to extract the complex permittivity of paper-based composites(PBCs). PBCs are ultrathin and flexible materials exhibiting considerable complex permittivity and dielectric loss tangent. Given the absence of mature measurement methods for PBCs and a lack of sufficient data for ANN training, a mapping relationship is initially established between the complex permittivity of honeycomb-structured microwave-absorbing materials(HMAMs, composed of PBCs) and that of PBCs using simulated data. Leveraging the ANN model, the complex permittivity of PBCs can be extracted from that of HMAMs obtained using standard measurement. Subsequently, two published methods are cited to illustrate the accuracy and advancement of the results obtained using the proposed approach. Additionally, specific error analysis is conducted, attributing discrepancies to the conductivity of PBCs, the homogenization of HMAMs, and differences between the simulation model and actual objects. Finally, the proposed method is applied to optimize the cell length parameters of HMAMs for enhanced absorption performance. The conclusion discusses further improvements and areas for extended research.

关键词： paper-based composite honeycomb complex permittivity artificial neural networks inverse modeling

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Neural network study of the nuclear ground-state spin distribution within a random interaction ensemble

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Nuclear Science and Techniques 2024年第3期35卷 216-227页

作者： Deng Liu Alam Noor A Zhen-Zhen Qin Yang Lei school of Mathematics and Physics Southwest University of Science and TechnologyMianyang 621010China school of Nuclear Science and technology Southwest University of Science and TechnologyMianyang 621010China

The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and the corresponding ground-state spins as labels or output *** quantum many-body system problem exceeds the capability of our optimized NNs in terms of accurately predicting the ground-state spin of each sample within the ***,our NN model effectively captured the statistical properties of the ground-state spin because it learned the empirical regularity of the ground-state spin distribution in TBRE,as discovered by physicists.

关键词： Neural network Two-body random ensemble Spin distribution of nuclear ground state

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